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Comparison

agentdojo vs dingo

Verdict

Pick agentdojo if agentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents; pick dingo if dingo includes a unique focus on multi-agent debate patterns ('Agent-as-a-Judge') for bias reduction and complex reasoning in evaluation tasks.

Markdown twin · agentdojo alternatives · dingo alternatives

GraphCanon updated 2w

agentdojo logo

agentdojo

ethz-spylab/agentdojo

★ 716pushed Jun 2, 2026
vs
dingo logo

dingo

MigoXLab/dingo

★ 733pushed Aug 6, 2026

Trust & integrity

Signalagentdojodingo
Maintenance
Steady (63d since push)
As of 2w · github_public_v1
Very active (0d since push)
As of 2w · github_public_v1
Provenance
Not a fork · Organization account
As of 2w · github_public_v1
Not a fork · Organization account
As of 2w · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of 1mo · osv@v1
No published findings from this source as of 2026-07-11
As of 1mo · osv@v1
deps.dev advisories
Not queried
deps.dev@v1
Not queried
deps.dev@v1
OpenSSF Scorecard
Not queried
openssf-scorecard@v1
Not queried
openssf-scorecard@v1
agentdojo trust reportdingo trust reportTrust methodology

Tagline

agentdojo
A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents
dingo
Dingo: A Comprehensive AI Data, Model and Application Quality Evaluation Tool

Stars

agentdojo
716
dingo
733

Forks

agentdojo
188
dingo
74

Open issues

agentdojo
41
dingo
4

Language

agentdojo
Python
dingo
Python

Adopt for

agentdojo
AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents.
dingo
Dingo includes a unique focus on multi-agent debate patterns ('Agent-as-a-Judge') for bias reduction and complex reasoning in evaluation tasks.

Persona

agentdojo
-
dingo
-

Runtime

agentdojo
-
dingo
-

License

agentdojo
MIT
dingo
Licensed under the Apache-2.0 license, it includes fasttext functionality for language detection, which itself is licensed under the MIT License.

Last pushed

agentdojo
Jun 2, 2026
dingo
Aug 6, 2026

Categories

agentdojo
AI Agents, Evaluation & Observability
dingo
Data & Retrieval, Evaluation & Observability

Trust and health

Maintenance

agentdojo
Steady (60%)
dingo
Very active (96%)

Days since push

agentdojo
63d
dingo
0d

Open issues (now)

agentdojo
41
dingo
4

OSV dependency advisories

agentdojo
No lockfile (source not queried)
dingo
No published findings from this source as of 2026-07-11

Full report

agentdojo
Trust report
dingo
Trust report

Choose agentdojo if…

  • License: agentdojo is MIT, dingo is Apache-2.0.
  • Pricing: Open-source under the MIT License. Some advanced features might require additional libraries or APIs..
  • Requirements: Min 8 GB RAM.
  • Tags unique to agentdojo: benchmark, large language models, prompt-injection, security.
  • Also covers AI Agents.
  • AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents.

When NOT to use agentdojo

  • AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism.
  • Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers.
GitHubDetails

Choose dingo if…

  • License: dingo is Apache-2.0, agentdojo is MIT.
  • Pricing: The tool currently offers free open-source options under an Apache 2.0 license with plans for future SaaS platform services that may come at a cost..
  • Tags unique to dingo: agent-as-a-judge, data-evaluation, data-quality, hallucination-detection.
  • Also covers Data & Retrieval.
  • When evaluating the quality of data, models, or applications that require insights from multiple perspectives to detect nuances such as bias or hallucination.

When NOT to use dingo

  • If your project does not benefit from a multi-agent approach for evaluation, and simpler single-model approaches suffice.
  • In scenarios where immediate feedback is critical but Dingo's planned SaaS platform with API access and dashboard support are still under development.
GitHubDetails

Explore

agentdojo trust report →dingo trust report →AI Agents category →Evaluation & Observability category →Data & Retrieval category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: agentdojo 716 · dingo 733 (synced Aug 5, 2026).

Common questions

What is the difference between agentdojo and dingo?
agentdojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents. dingo: Dingo: A Comprehensive AI Data, Model and Application Quality Evaluation Tool. See the comparison table for live GitHub stats and shared categories.
When should I choose agentdojo over dingo?
Choose agentdojo over dingo when License: agentdojo is MIT, dingo is Apache-2.0; Pricing: Open-source under the MIT License. Some advanced features might require additional libraries or APIs.; Requirements: Min 8 GB RAM; Tags unique to agentdojo: benchmark, large language models, prompt-injection, security; Also covers AI Agents; AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents.
When should I choose dingo over agentdojo?
Choose dingo over agentdojo when License: dingo is Apache-2.0, agentdojo is MIT; Pricing: The tool currently offers free open-source options under an Apache 2.0 license with plans for future SaaS platform services that may come at a cost.; Tags unique to dingo: agent-as-a-judge, data-evaluation, data-quality, hallucination-detection; Also covers Data & Retrieval; When evaluating the quality of data, models, or applications that require insights from multiple perspectives to detect nuances such as bias or hallucination.
When should I avoid agentdojo?
AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers.
When should I avoid dingo?
If your project does not benefit from a multi-agent approach for evaluation, and simpler single-model approaches suffice. In scenarios where immediate feedback is critical but Dingo's planned SaaS platform with API access and dashboard support are still under development.
Is agentdojo or dingo more popular on GitHub?
dingo has more GitHub stars (733 vs 716). Stars measure visibility, not whether either tool fits your constraints.
Are agentdojo and dingo open source?
Yes - both are open-source projects on GitHub (agentdojo: MIT, dingo: Apache-2.0).
Where can I find alternatives to agentdojo or dingo?
GraphCanon lists graph-backed alternatives at agentdojo alternatives and dingo alternatives (agentdojo markdown twin, dingo markdown twin), ranked by typed relationship edges rather than popularity votes.
Is there a machine-readable version of this comparison?
Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
Which is better maintained, agentdojo or dingo?
agentdojo: Steady. dingo: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
Where are the full trust reports for agentdojo and dingo?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: agentdojo trust report; dingo trust report.

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